Machine learning approach of automatic identification and counting of blood cells (RBC, WBC, and Platelet) with KNN and IOU based verification.

The complete blood count (CBC) dataset contains a total of 360 blood smear images of red blood cells (RBCs), white blood cells (WBCs), and Platelets with annotations.

[ICIP'24 Lecture Presentation] Official implementation of "CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer".

This is a dataset of blood cells photos.

This is our repo for CS231 - Computer Vision, Spring 2021, University of Information Technology, VNU HCM

This is an individual project on Image Processing and Computer Vision course about blood cells object detection and classification.

A Flask web app integrating YOLOv8 by Ultralytics to detect and count blood cells.

Biomedical Image Processing BAP (Scientific Research Project) - Piri Reis University

Detects blood cells in images using a YOLOv8 model. Interactive web app built with Gradio and deployed on Hugging Face Spaces.

Blood Cell Detection with YOLOv10: This project utilizes YOLOv10, a cutting-edge object detection model, to accurately identify and count blood cells in images. Integrated with a Gradio web interface, the tool provides real-time image annotation and detailed cell count summaries, making it a powerful solution for automated blood cell analysis.

Automated blood cell detection system using ASP.NET web interface and Python machine learning models. Features real-time image analysis, user authentication, and ML-powered cell classification with Keras/TensorFlow.

Blood sample management and analysis of multiple brands of Hematology Devices

Code for the paper titled "Advancing instance segmentation and WBC classification in peripheral blood smear through domain adaptation: A study on PBC and the novel RV-PBS datasets" published on Elsevier's Expert Systems With Applications (ESWA) journal.

Project for the Applied Machine Learning (EI70360) class at TUM Summer Semester 2023.

Analyze mobile network signals in real time with Cell-ID for detailed insight into signal strength, cell history, and network parameters across devices.